Data Scientist - Financial Fraud Detection at Kuda Bank

Role Overview:

We are seeking a highly skilled and experienced Data Scientist to join our Decision Science team, specializing in the development and deployment of advanced fraud detection models. In this critical role, you will play a key part in safeguarding our customers and the bank from financial losses by identifying and mitigating fraudulent activities across various channels. You will work with vast, complex transactional datasets, collaborate closely with Fraud Operation and Credit Risk and Compliance teams, and contribute to the continuous evolution of our fraud prevention strategies in a highly regulated environment.

Roles and responsbilites:

• Design, develop, and implement machine learning models tailored for banking fraud detection, including  anomaly detection, transaction fraud, account takeover, application fraud, and money laundering detection.
• Deploy and monitor real-time fraud detection systems, ensuring high performance and minimal latency.
• Continuously evaluate and refine models to improve accuracy and efficiency.
• Develop models that adhere to regulatory requirements and compliance standards (e.g., AML, KYC).
• Adapt models to the ever-changing tactics of financial criminals.
• Analyze large volumes of transactional data, customer behavior data, and external data sources to identify fraud patterns and anomalies.
• Conduct in-depth analysis of financial transactions to detect suspicious activities and potential fraud risks.
• Develop and maintain data pipelines for fraud-related data, ensuring data integrity and compliance.
• Collaborate with fraud investigators, risk managers, compliance officers, and technology teams to develop and implement effective fraud prevention solutions.
• Communicate complex model insights and fraud trends to stakeholders, including senior management and regulatory bodies.
• Participate in cross-functional projects related to financial crime prevention and risk management.
• Stay abreast of emerging fraud trends and regulatory changes in the banking industry.
• Research and evaluate new machine learning techniques and technologies for fraud detection in the financial sector.
• Contribute to the development of innovative solutions for combating financial crime.

Requirements

• Degree in Computer Science, Statistics, Mathematics, Finance, or a related quantitative field.
• A Master's or Ph.D. degree is desirable but not required.
• Minimum of 3 years of experience as a data scientist, within the banking or financial services industry.
• Proven experience in developing and deploying machine learning models for transaction monitoring, customer behaviour.
• Familiarity with Credit Product construct.
• Familiarity with regulatory requirements and compliance standards related to financial crime (e.g., AML, KYC, GDPR).
• Strong proficiency in Python and relevant machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Expertise in SQL and experience with large-scale relational databases.
• Experience with fraud detection platforms and tools is desirable
• Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and big data technologies (e.g., Spark, Hadoop).
• Knowledge of statistical analysis, time series analysis, and network analysis.
• Strong analytical and problem-solving skills, with a focus on detail and accuracy.
• Excellent communication and presentation skills, with the ability to convey complex information to diverse audiences.
• Ability to work effectively in a fast-paced, regulated environment.
• Strong understanding of banking operations and financial products.